The evolution of the web to a semantic web is gaining momentum. Resource Description Framework (RDF) is being widely adopted as a standard to capture the semantics of data. Facts represented as RDF (subject, predicate, object) triples can capture both relationships between resources as well as attribute values associated with a resource. A unique challenge of semantic data stores is the ability to automatically derive additional facts based on facts already asserted in the semantic model. These additional facts are derived using inference rules that model the knowledge contained in the existing facts in a process called entailment. With large semantic data models, firing the inference rules to generate inferred triples is a processor-intense and time consuming process. For example, using a typical PC with 2.4 GHz processor, 8 GB main memory and 3 TB in disk memory, firing approximately 50 inference rules to entail the LUBM8000 ontology, a benchmark that includes more than a billion triples, results in about 42 hours of processing time using Oracle Database 11.1.